SoroushVahidi/scidocs huggingface dataset:
Game strategies in network security
(2002)

This paper presents a game-theoretic method for analyzing the security of computer networks. We view the interactions between an attacker and the administrator as a two-player stochastic game and construct a model for the game. Using a nonlinear program, we compute Nash equilibria or best-response strategies for the players (attacker and administrator). We then explain why the strategies are realistic and how administrators can use these results to enhance the security of their network.
Data mining in metric space: an empirical analysis of supervised learning performance criteria
(2004)

Many criteria can be used to evaluate the performance of supervised learning. Different criteria are appropriate in different settings, and it is not always clear which criteria to use. A further complication is that learning methods that perform well on one criterion may not perform well on other criteria. For example, SVMs and boosting are designed to optimize accuracy, whereas neural nets typically optimize squared error or cross entropy. We conducted an empirical study using a variety of learning methods (SVMs, neural nets, k-nearest neighbor, bagged and boosted trees, and boosted stumps) to compare nine boolean classification performance metrics: Accuracy, Lift, F-Score, Area under the ROC Curve, Average Precision, Precision/Recall Break-Even Point, Squared Error, Cross Entropy, and Probability Calibration. Multidimensional scaling (MDS) shows that these metrics span a low dimensional manifold. The three metrics that are appropriate when predictions are interpreted as probabilities: squared error, cross entropy, and calibration, lay in one part of metric space far away from metrics that depend on the relative order of the predicted values: ROC area, average precision, break-even point, and lift. In between them fall two metrics that depend on comparing predictions to a threshold: accuracy and F-score. As expected, maximum margin methods such as SVMs and boosted trees have excellent performance on metrics like accuracy, but perform poorly on probability metrics such as squared error. What was not expected was that the margin methods have excellent performance on ordering metrics such as ROC area and average precision. We introduce a new metric, SAR, that combines squared error, accuracy, and ROC area into one metric. MDS and correlation analysis shows that SAR is centrally located and correlates well with other metrics, suggesting that it is a good general purpose metric to use when more specific criteria are not known.
Navigation in a small world
(2000)

The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology. Experimental study of the phenomenon revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed, and here I investigate the second by modelling how individuals can find short chains in a large social network.
An incremental strategy for tractor-trailer vehicle global trajectory optimization in the presence of obstacles
(2015)

Trajectory planning is a critical aspect in driving an autonomous tractor-trailer vehicle. This study considers trajectory planning as a minimal-time optimal control problem that incorporates the kinematics, mechanical/physical constraints, environmental requirements and an optimization criterion. This formulation benefits in the ability to directly handle time-dependent requirements. A gradient-based numerical solver is adopted to tackle the formulated optimal control problem. An incremental strategy is proposed to enhance the global optimization ability of that solver and to help find satisfactory solutions in challenging cases. Specifically, when an optimal solution is found, we doubt whether that is merely a local optimum and then still expect to make evolution; when optimal solutions are not easy to obtain, we expect at least one feasible solution first, which is taken as a preliminary guess to assist the subsequent optimization process. Both procedures proceed alternatively until no progress can be made any further. Some intricate simulation results are well beyond manual operation ability. Moreover, our overall proposal, as a unified and open framework, can deal with a wide variety of user-specified requirements.
3D video fragments: dynamic point samples for real-time free-viewpoint video
(2004)

We present 3D video fragments, a dynamic point sample framework for real-time 3D video. By generalizing 2D video pixels towards 3D irregular point samples we combine the simplicity of conventional 2D video processing with the power of more complex polygonal representations for 3D video. We propose a differential update scheme exploiting the spatio-temporal coherence of the video streams of multiple cameras. Updates are issued by operators such as inserts and deletes accounting for changes in the input video images. The operators from multiple cameras are processed, merged into a 3D video stream and transmitted to a remote site. We also introduce a novel concept for camera control which dynamically selects the set of relevant cameras for reconstruction. Moreover, it adapts to the processing load and rendering platform. Our framework is generic in the sense that it works with any realtime 3D reconstruction method which extracts depth from images. The video renderer displays 3D videos using an efficient point based splatting scheme and makes use of state-of-the-art vertex and pixel processing hardware for real-time visual processing.
Education , politics and opinions about climate change evidence for interaction effects
(2009)

U.S. public opinion regarding climate change has become increasingly polarized in recent years, as partisan think tanks and others worked to recast an originally scientific topic into a political wedge issue. Nominally “scientific” arguments against taking anthropogenic climate change seriously have been publicized to reach informed but ideologically receptive audiences. Reflecting the success of such arguments, polls have noted that concern about climate change increased with education among Democrats, but decreased with education among Republicans. These observations lead to the hypothesis that there exist interaction (non-additive) effects between education or knowledge and political orientation, net of other background factors, in predicting public concern about climate change. Two regional telephone surveys, conducted in New Hampshire (n = 541) and Michigan (n = 1, 008) in 2008, included identical climate-change questions that provide opportunities to test this hypothesis. Multivariate analysis of both surveys finds significant interactions. These empirical results fit with theoretical interpretations and several other recent studies. They suggest that the classically identified social bases of concern about the environment in general, and climate in particular, have shifted in recent years. Narrowcast media, including the many Web sites devoted to discrediting climatechange concerns, provide ideal conduits for channeling contrarian arguments to an audience predisposed to believe and electronically spread them further. Activeresponse Web sites by climate scientists could prove critical to counterbalancing contrarian arguments. L. C. Hamilton (B) Sociology Department, University of New Hampshire, Durham, NH, 03824, USA e-mail: Lawrence.Hamilton@unh.edu
Male-to-female transsexualism: technique, results and 3-year follow-up in 50 patients.
(2010)

AIM
To evaluate the functional and cosmetic results of male-to-female gender-transforming surgery.


PATIENTS AND METHODS
Between May 2001 and April 2008 we performed 50 male-to-female gender-transforming surgeries. All patients had been cross-dressing, living as women, and receiving estrogen and progesterone for at least 12 months, which was sufficient for breast development and atrophy of the testes and prostate to occur. This hormonal therapy was suspended 1 month before the operation.


RESULTS
The mean operative time was 190 min and the mean depth of the vagina was 10 cm. On follow-up, the most common complication (10%) was shrinkage of the neovagina, which could be corrected by a second surgical intervention. Of the 50 patients, 45 (90%) were satisfied with the esthetic results; 42 patients (84%) reported having regular sexual intercourse, 2 of whom had pain during intercourse. Of the 50 patients, 35 (70%) reported achieving clitoral orgasm.


CONCLUSION
Male-to-female gender-transforming surgery can assure satisfactory cosmetic and functional results, with a reduced intra- and postoperative morbidity. Nevertheless the experience of the surgeon and the center remains central to obtaining optimal results.
Forecasting short-term taxi demand using boosting-GCRF
(2017)

It will be most efficient to frame operation strategies before actual taxi demand is revealed. But this is challenging due to limited knowledge of the taxi demand distribution in immediate future and is more prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the short-term taxi demand distribution using historical time-series demand over the study area. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to five other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best modified mean absolute percentage error being 10.4%. The approach is observed to be robust based on its prediction performance on anomaly taxi demand data. In addition, the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.
Factorized adaptation for deep neural network
(2014)

In this paper, we propose a novel method to adapt context-dependent deep neural network hidden Markov model (CD-DNN-HMM) with only limited number of parameters by taking into account the underlying factors that contribute to the distorted speech signal. We derive this factorized adaptation method from the perspectives of joint factor analysis and vector Taylor series expansion, respectively. Evaluated on Aurora 4, the proposed method can get 19.0% and 10.6% relative word error rate reduction on test set B and D with only 20 adaptation utterances, and can have decent improvement with as few as two adaptation utterances. We also show that the proposed method is better than feature discriminative linear regression (fDLR), an existing DNN adaptation method. Its small number of parameters and short training time offer an attractive solution to low-footprint speech applications.
DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks
(2017)

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.
How does sexual minority stigma "get under the skin"? A psychological mediation framework.
(2009)

Sexual minorities are at increased risk for multiple mental health burdens compared with heterosexuals. The field has identified 2 distinct determinants of this risk, including group-specific minority stressors and general psychological processes that are common across sexual orientations. The goal of the present article is to develop a theoretical framework that integrates the important insights from these literatures. The framework postulates that (a) sexual minorities confront increased stress exposure resulting from stigma; (b) this stigma-related stress creates elevations in general emotion dysregulation, social/interpersonal problems, and cognitive processes conferring risk for psychopathology; and (c) these processes in turn mediate the relationship between stigma-related stress and psychopathology. It is argued that this framework can, theoretically, illuminate how stigma adversely affects mental health and, practically, inform clinical interventions. Evidence for the predictive validity of this framework is reviewed, with particular attention paid to illustrative examples from research on depression, anxiety, and alcohol-use disorders.
Research on significance of VCPU scheduling for SR-IOV on NUMA platform
(2014)

Virtualization is vital important in server consolidation. Using virtualization technology, a single physical machine can provide multiple isolated virtual machine for users, which will improve the overall system resources usage. However, In virtualization environment, Non-Uniform Memory Access Architecture (NUMA) problem is also a very important parameter that affects server's performance. To solve NUMA problem, many solutions have been proposed to reduce its performance impact on virtual machines and improve the overall system performance. Hardware Single Root I/O Virtualization (SR-IOV) is a solution for high performance network processing, but when SR-IOV technology is running on a NUMA platform, it also generates the traditional problem about NUMA.
Real-time procedural generation of 'pseudo infinite' cities
(2003)

We present an approach to procedural generation of `pseudo infinite' virtual cities in real-time. The cities contain geometrically varied buildings that are generated as needed. The building generation parameters are created by a pseudo random number generator, seeded with an integer derived from the building's position. The varied building geometries are extruded from a set of floor plans. The floor plans for each building are created by combining randomly generated polygons in an iterative process. A display list caching and frustum filling approach manages the generation of buildings and the use of system resources. This approach has been implemented on commodity PC hardware, resulting in interactive frame rates.
Hard Words
(2004)

How do children acquire the meaning of words? And why are words such as know harder for learners to acquire than words such as dog or jump? We suggest that the chief limiting factor in acquiring the vocabulary of natural languages consists not in overcoming conceptual difficulties with abstract word meanings but rather in mapping these meanings onto their corresponding lexical forms. This opening premise of our position, while controversial, is shared with some prior approaches. The present discussion moves forward from there to a detailed proposal for how the mapping problem for the lexicon is solved, as well as a presentation of experimental findings that support thisaccount.Wedescribeanoverlappingseriesofsteps throughwhichnovicesmove in representing the lexical forms and phrase structures of the exposure language, a probabilistic multiple-cue learning process known as syntactic bootstrapping. The machinery is set inmotionbyword-to-worldpairing, aprocedureavailable tonovices fromthe LANGUAGE LEARNING AND DEVELOPMENT, 1(1), 23–64 Copyright © 2005, Lawrence Erlbaum Associates, Inc.
Virtual Machine Migration in Cloud Computing
(2015)

Cloud computing is the delivers the computing services over the internet. Cloud services help individuals and organization to use data that are managed by third parties or another person at remote locations. Virtual Machine (VM) is an emulation of a particular computer system. In cloud computing, Virtual machine migration is a useful tool for migrating Operating System instances across multiple physical machines. It is used to load balancing, fault management, low-level system maintenance and reduce energy consumption. There are various techniques and parameters available for VM migration. This paper presents the various virtual machine migration techniques.
The KUKA Control Toolbox: motion control of KUKA robot manipulators with MATLAB
(2010)

The KUKA Control Toolbox (KCT) is a collection of MATLAB functions developed at the University of Siena, for motion control of KUKA robot manipulators. The toolbox, which is compatible with all 6 DOF small and low-payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 40 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display, 3-D animation and diagnostics. Applicative examples show the flexibility of KCT and its easy interfacing with other toolboxes and external devices.
Saliency Detection via Absorbing Markov Chain
(2013)

In this paper, we formulate saliency detection via absorbing Markov chain on an image graph model. We jointly consider the appearance divergence and spatial distribution of salient objects and the background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The absorbed time of transient node measures its global similarity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth background regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the proposed method against the state-of-the-art methods.
Human Activity Recognition and Pattern Discovery
(2010)

In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and healthcare. This article focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research and the nature of human activities poses different challenges. Human activity understanding encompasses activity recognition and activity pattern discovery. The first focuses on accurate detection of human activities based on a predefined activity model. An activity pattern discovery researcher builds a pervasive system first and then analyzes the sensor data to discover activity patterns.
Soft Biometrics in Online Social Networks: A Case Study on Twitter User Gender Recognition
(2017)

The proliferate unstructured data generated in online social networks leads to significant research advances in the recognition of user profiles (e.g., age, gender, ethnicity, etc.), but meanwhile brings new challenges. These attributes are referred to as soft biometrics and provide a semantic description of users. Identifying users' soft biometric traits in online social networks is crucial for a variety of applications such as customized marketing, personalized recommendation, and urban planning. Compared to conventional studies, we address the importance and challenges of identifying soft biometrics in online social networks and provide a case study on gender recognition of Twitter users based on their tweets texts and profile images. We first apply an efficient approach to label the soft biometric attributes of users using their self-reported names, instead of labor-forced methodology such as manually labeling and crowdsourcing. Then we investigate the approaches using texts and profile images, respectively, including Hashtag-based TF-IDF, LDA Topic Distribution, SIFT-based TF-IDF and Convolutional Neural Networks (CNN). Finally, we propose an ensemble method by combining models learned from both texts and images to enhance gender identification. The performance in our extensive experiments is demonstrated to be comparative with state-of the-art work.
A Beginner's Guide to Interferometric SAR Concepts and Signal Processing [AESS Tutorial IV]
(2007)

Interferometric synthetic aperture radar (IFSAR, also abbreviated as InSAR) employs pairs of high resolution SAR images to generate high quality terrain elevation maps using phase interferometry methods. IFSAR provides an all-weather, day/night capability to generate measurements of terrain elevation on a dense grid of sample points with accuracies of ones of meters. Both spaceborne and airborne IFSAR systems are in use. In this paper we present a tutorial introduction to the concepts, techniques, and applications of IFSAR. After a brief introduction to digital elevation models (DEMs) and digital terrain elevation data (DTED), the fundamental IFSAR equation relating interferometric phase measurements to terrain elevation is derived from simple geometric considerations. The central section of the paper describes the major algorithmic steps required to form an IFSAR terrain map. Finally, variations of IFSAR for mapping terrain elevation or reflectivity changes are briefly described. A Web site at users.ece.gatech.edu/~mrichard/AESSJFSAR.htm provides access to color versions of many of the IFSAR images included in this paper.
Toward Optimal Pedagogical Action Patterns by Means of Partially Observable Markov Decision Process
(2016)

Good pedagogical actions are key components in all learning-teaching schemes. Automate that is an important Intelligent Tutoring Systems objective. We propose apply Partially Observable Markov Decision Process (POMDP) in order to obtain automatic and optimal pedagogical recommended action patterns in benefit of human students, in the context of Intelligent Tutoring System. To achieve that goal, we need previously create an efficient POMDP solver framework with the ability to work with real world tutoring cases. At present time, there are several Web available POMDP open tool solvers, but their capacity is limited, as experiments showed in this paper exhibit. In this work, we describe and discuss several design ideas toward obtain an efficient POMDP solver, useful in our problem domain.
Verb Semantics and Lexical Selection
(1994)

This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.